In Table REF  we report the results. It is clear from the table that adding the contrastive loss indeed helps in localization. The improvement is much more prominent on the real-life image manipulation dataset IMD-20. Contrastive loss helps to improve the AUC score by \(4.7\%\) . It should be noted that without the contrastive loss our method already achieves very good results. The reason is that our model is similar to RGB-N [1]} in the regard that we also use two stream network, i.e, RGB and SRM streams. In addition, we carefully supplement our network with ASPP module and Deeplab decoder head, which helps to improve the overall performance compared to RGB-N. Using contrastive loss further improves our results and helps to outperform all the other baseline models.
